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Heart Failure Management:
Integration of Device Sensor Data
into Clinical Practice
William T. Abraham, MD, FACP, FACC, FAHA
Professor of Medicine, Physiology, and Cell Biology
Chair of Excellence in Cardiovascular Medicine
Chief, Division of Cardiovascular Medicine
Deputy Director, Davis Heart & Lung Research Institute
The Ohio State University
Columbus, Ohio
Background
• Despite current therapies and disease
management approaches, the rate of heart
failure hospitalization remains unacceptably
high
• > 1.1 million heart failure hospitalizations annually
• > 25% readmission rate at 1 month; 50% at 6 months
• > $18 billion in annual direct costs
• Current methods for monitoring heart failure
patients have not adequately addressed this
issue
Limitations of Available Monitoring Systems
• Weight and Symptoms – Recent large, landmark clinical
studies (Tele-HF, TIM-HF) investigating the effectiveness
of telemonitoring demonstrated no benefit in reducing HF
hospitalizations
• BNP - PRIMA Study guided identification of patients at
risk for HF events, but showed no significant reduction in
HF-related admissions
• Device-Based Diagnostics - May be useful for identifying
patients that may be at higher risk for a HF
hospitalization(PARTNERS-HF Study), but have not yet
demonstrated a reduction in HF-related hospitalizations
Tele-HF: Yale Heart Failure Telemonitoring Study; NEJM, 2010
TIM-HF: Telemonitoring Intervention in Heart Failure, Eur J. Heart Failure, 2010
PRIMA: Can Pro-BNP guided heart failure therapy improve morbidity and mortality? J Am Coll Card, 2010
PARTNERS-HF: Combined Heart Failure Device Diagnostics Identify Patients at Higher Risck of Subsequent
Heart Failure Hospitalizations. J Am Coll Card, 2010
Background
• Workflow issues add to the burden of follow-up
care and impair adequate disease management
• 50% of patients admitted to the hospital have no
contact with a physician within 30 days following
discharge
• Typical in-office visits are an impractical way to
assure prompt and ongoing patient follow-up
• Thus, there is a critical need for enhanced
means to monitor patients and to streamline
workload
Device-Based Home Monitoring Systems
• Objectively track physiological measures of
relevance longitudinally over time
• Report values and trends to the clinician remotely
via web-based information systems
• Provide thresholds or algorithms for “early warning”
• Evaluate the efficacy of acute and chronic
treatments
• Empower clinicians and patients to better manage
their heart failure
Implantable CRT and ICD Devices May Offer
Unique Means to Monitor Heart Failure Patients
Atrial
Depolarization
Heart rate
AFIB/ATACH
APACE
Ventricular Rate
Response
Heart rate
VT/VF
VPACE
Patient Activity
Heart Rate
Variability
Impedance
(fluid status,
respiration)
Physiological Premise of Implantable HF
Device Diagnostics (1)
-21 -14 - 7 Days
ReactiveProactive
0
Symptoms
Physiologic Changes
Heart Failure Event
Physiological Premise of Implantable HF
Device Diagnostics (2)
-21 -14 - 7 Days
Proactive
0
Physiologic Changes
Medical InterventionAverted
Heart Failure Event
Heart Rate Variability and Patient Activity as
Measures of Heart Failure Clinical Status
Autonomic Mechanisms in Heart Failure
• Sympathetic activation and parasympathetic
withdrawal occur in the setting of heart failure
• Heart rate variability (HRV) provides an
integrated measure of autonomic function or
the balance between these two systems
• Low HRV – indicative of high sympathetic and
low parasympathetic activity – is associated
with poor outcomes in heart failure patients
Continuous HRV Before Hospitalization
Adamson PB et al Circulation 2004;110:2389-2394
Heart
Rate
Variabili
ty (
ms)
Nig
ht
Heart
Rate
(B
PM
)
Patient A
ctivity
(min
ute
s/d
ay)
Days Relative to Hospital Admission
-80 -60 -40 -20 0 2060
70
80
-80 -60 -40 -20 0 20140
160
180
200
220
-80 -60 -40 -20 0 2072
74
76
78
80
Intrathoracic Impedance as a Measure
of Heart Failure Clinical Status
“Wetter” Lungs
Impedance Decreases with
Increasing Lung Wetness
Superior Performance of an Intrathoracic
Impedance-Derived Fluid Index Versus Daily
Weight Monitoring in Heart Failure Patients:
Results of the Fluid Accumulation Status Trial
(FAST)
William T. Abraham, Steven Compton, Blair Foreman,
Garrie Haas, Robert C. Canby, Robert Fishel, Scott
McRae, Gloria B. Toledo, and Shantanu Sarkar
On behalf of the FAST Study Group
Presented at the Annual Meeting of the Heart Failure Society of America,
September 2009
FAST Results:
Accuracy of Fluid Index vs. Weight at
Nominal Thresholds
Data are Generalized Estimator Equation (GEE) adjusted estimates (95% Confidence Interval)
Fluid Index Threshold = 60 Ω•days
Weight Threshold = 3lbs in one day or 5 lbs in 3 days
Fluid Index* Weight† p
Sensitivity (%) 76.4 (60.8, 87.1) 22.5 (12.5, 37.1) <0.0001
Unexplained Detection Rate (/pt/yr) 1.9 (1.7, 2.1) 4.3 (3.2, 5.8) <0.0001
FAST: Results
Unexplained Detections (subject-1yr-1)
0 2 4 6 8 10 12 14 16
Sensitiv
ity (
%)
0
20
40
60
80
100
Fluid Index
Weight
nominal
Se
nsitiv
ity (
%)
Chair: Maier, Würzburg, Germany
ClinicalTrials.gov, NCT00711360
Monitoring of Fluid Status in Heart Failure Patients
by Intrathoracic Impedance Measurement (Home CARE II)
Enrollment planned:
300 pts. with ICD/CRT-D indication, NYHA II-IV, EF ≤35%
increased risk for HF related hospitalizations
Objective:
Development of an intrathoracic impedance based detection
algorithm as a predictor for clinically relevant HF events
ICD/CRT: Prediction of HF Events
300 pts. with CRT-ICD,
documented episode
of paroxysmal AF
Atrial Tachyarrhythmias
Home Monitoring
activated
Control
HM deactivated
R
Composite endpoint: CV mortality or hospitalization,
inappropriate ICD therapy
Clinical Effect of HF Management via HM with a Focus on AF
(effecT)
Follow-up: 12 months
Chair: Schalij, Leiden, The Netherlands
ClinicalTrials.gov, NCT00811382
Some Ongoing Home Monitoring
Trials
Acronym n = Device Pts. Endpoint
VIRTUE 120 PM DDD, no AF workload
QUANTUM 150 ICD prim./second. anxiety
Euro-Eco 312 ICD prim./second. FU costs
SPIRIT-ICD 500 ICD MADIT II mortality
IMPACT 2718 ICD/CRT CHADS2 ≥1 embolic events
HOME-CARE II 300 ICD/CRT any ICD HF hospital
IN-TIME 620 ICD/CRT chronic HF HF events
effecT 300 CRT-D parox. AF CV hospital
Castle-AF 420 Ablation/ICD AF+NYHA≥II HF hospital
Total 5,440
Implantable Hemodynamic Monitors
LV Pressure Sensor
PA Pressure Sensors
RV Pressure Sensors
LA Pressure Sensor
The Pulmonary Artery Pressure
Measurement System*
Catheter-based delivery system
*CardioMEMS Inc., Atlanta, Georgia, USA
MEMS-based pressure sensor
Home electronics
PA Measurement database
Primary Results of the CardioMEMS Heart
Sensor Allows Monitoring of Pressure to
Improve Outcomes in NYHA Class III Heart
Failure Patients (CHAMPION) Trial
0
20
40
60
80
100
120
140
160
180
200
220
240
260
0 90 180 270 360 450 540 630 720 810 900
Treatment Control
6 Months 15 Months
Cumulative HF Hospitalizations Over Entire
Randomized Follow-Up Period
p < 0.001, based on Negative Binomial Regression
Cum
ula
tive N
um
ber
of H
F H
ospitaliz
ations
Days from Implant
At Risk
Treatment 270 262 244 209 168 130 107 81 28 5 1
Control 280 267 252 215 179 138 105 67 25 10 0
Circulation 2010; 121:1086-1095
HF Event Rates
(ADHF and All-Cause Death)Comparison of Periods with and without LAP-Guidance
Period Annualized Event
Rate
P-values
12-mo period before
enrollment
1.4 (1.1-1.9)
0.054
First 3 mo
Observation Period
0.68 (0.33-1.4) <0.001
0.041
After mo 3
Titration/Stability
Periods
0.28 (0.18-0.45)
Ritzema J, et al. Physician-Directed Patient Self-Management of Left Atrial
Pressure in Advanced Heart Failure. Circulation 2010;121:1086-1095.
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